Design Principles for Multi-Database Applications

12002

Introduction to This Lesson

In this lesson, we will explore the concept of multi-database design, the importance of data warehouses in enterprise applications, and the synchronization mechanisms of data warehouses. We will focus on the critical role that data warehouses play in enterprise applications and the principles of view design in multi-database applications, particularly the necessity of creating views using data from the data warehouse. Additionally, we will delve into the synchronization mechanisms of data warehouses, especially how triggers work to synchronize data within the data warehouse.

1. Multi-Database Design

In enterprise application development, multi-database design can play an important role in achieving unified management and data integration of business data. Through multi-database design, enterprises can store and manage data from different departments and business areas in a unified manner, thereby supporting diverse application service needs.

Use Cases for Multi-Databases

In enterprise applications, human resource management systems, financial systems, and inventory management systems are common key business systems that typically require the use of multiple databases to support complex data management and business needs.

Taking the human resource management system as an example: human resource management encompasses multiple modules, such as personnel information management, employee training, and recruitment. Each module contains specific functionalities, for instance, the personnel information management module includes employee profile management, attendance records, and payroll functions; the employee training module includes training plans and performance evaluations. To meet these needs and optimize system performance, we should store the data of different functional modules in separate databases to achieve data isolation and enhance data security. Therefore, adopting a multi-database design is necessary.

Concept of Data Warehouse

Within multi-databases, there is another important concept: the data warehouse. A multi-database system means that an enterprise may have multiple databases storing data from different departments or business functions, which may have inconsistent formats and be difficult to integrate.

When applications used by the enterprise need to store data from multiple different databases, the data warehouse plays an important role, similar to a large data center. It is responsible for integrating, cleaning, and transforming data from various databases and then storing it in a unified location for analysis and a comprehensive understanding of the overall situation.

Regardless of which database the data was originally stored in, it can be more conveniently viewed through the data warehouse, allowing the enterprise to gain a more comprehensive understanding of the relationships and trends among the data.

In summary, the role of the data warehouse in a multi-database environment is to help enterprises integrate data, provide a comprehensive data view, and support cross-database data analysis, thereby better managing data and making more informed decisions.

2. Use of Data Warehouse in Enterprise Applications

Taking the inventory management system as an example, let’s see how to utilize the data warehouse to optimize business processes and decision-making, to better understand the use of data warehouses in enterprise applications.

Suppose a company uses an inventory management system to manage its supply chain department, inventory management department, and sales department, with each department having its own database for storing relevant business data.

Supply Chain Department Database: Contains supplier information, purchase orders, delivery records, etc.
Inventory Management Department Database: Contains inventory records, product information, inbound and outbound records, etc.
Sales Department Database: Contains customer information, sales orders, delivery records, etc.

By establishing a data warehouse to integrate data from various departments, we can summarize the functions and advantages of the data warehouse in the inventory management system as follows:

By using the data warehouse, the company can better manage its supply chain, inventory, and sales activities, optimize business processes, and make more informed decisions, thus improving efficiency and reducing costs.

Using Data from the Data Warehouse as Views

In multi-database applications, to avoid potential data inconsistencies or errors caused by direct cross-database queries, it is crucial to choose to use data from the data warehouse as views.

Synchronization of Business Data Tables Across Multiple Applications:

The data warehouse, as a centralized database for storing data, can be used to synchronize business data tables from multiple applications. Through the process of Extract, Transform, Load (ETL), the business data from each application can be synchronized to the data warehouse, achieving centralized management and a unified source of data.

Unified Source of User Data Across Multiple Applications:

The data warehouse can also serve as a unified source of user data for multiple applications. By centrally managing user information, including user identities and permissions, a shared user system across multiple applications can be achieved, improving consistency and efficiency in user management.

Real-Time Data Updates Across Multiple Applications:

The data warehouse can support real-time data updates, ensuring that the data in the views remains current. Through regular data synchronization and update processes, users can always access the latest data.

Data Security and Privacy Protection Across Multiple Applications:

The views in the data warehouse can implement data security and privacy protection. By setting permissions and access controls, it can ensure that only authorized users can access specific data, thereby protecting sensitive information from unauthorized access.

4. Data Warehouse Synchronization Mechanism: Triggers

The data warehouse synchronization mechanism is a key component in ensuring that the data in the data warehouse remains synchronized with the source systems. During the data warehouse synchronization process, we use MySQL triggers to automatically execute specific operations when certain events occur in the database tables, achieving data synchronization.

When using MySQL triggers for data warehouse synchronization, the general steps include:

Creating Triggers

First, triggers need to be created in the MySQL database. Triggers can be defined for specific tables and events (such as BEFORE INSERT, AFTER UPDATE, AFTER DELETE).

Writing Trigger Logic

When creating triggers, the logic code for the trigger needs to be written, defining the operations that should be executed when the triggering event occurs. These operations typically include extracting, transforming, and loading data from the source system tables into the corresponding tables in the data warehouse.

Activating Triggers

Once the trigger is created and bound to the corresponding tables and events, the trigger will be activated when these events occur, executing the defined logic code.

Data Synchronization

The logic code of the trigger will execute data synchronization operations, ensuring that the data in the data warehouse remains synchronized with the source systems. This helps maintain data consistency and real-time accuracy.

Performance Optimization

When using MySQL triggers for data synchronization, performance optimization must be considered to ensure that the trigger logic is concise and efficient, avoiding unnecessary impacts on database performance.

In summary, the principle of triggers synchronizing data in the data warehouse is based on monitoring data change events and executing corresponding synchronization operations when events occur, ensuring that the data in the data warehouse remains consistent with the source systems. By designing and using triggers appropriately, real-time data synchronization can be achieved, improving the data quality and timeliness of the data warehouse.

## Introduction to This Lesson

In this lesson, we will explore the concept of multi-database design, the importance of data warehouses in enterprise applications, and the synchronization mechanisms of data warehouses. We will focus on the critical role that data warehouses play in enterprise applications and the principles of view design in multi-database applications, particularly the necessity of creating views using data from the data warehouse. Additionally, we will delve into the synchronization mechanisms of data warehouses, especially how triggers work to synchronize data within the data warehouse.

## 1. Multi-Database Design

In enterprise application development, multi-database design can play an important role in achieving unified management and data integration of business data. Through multi-database design, enterprises can store and manage data from different departments and business areas in a unified manner, thereby supporting diverse application service needs.

### Use Cases for Multi-Databases

In enterprise applications, human resource management systems, financial systems, and inventory management systems are common key business systems that typically require the use of multiple databases to support complex data management and business needs.

Taking the human resource management system as an example: human resource management encompasses multiple modules, such as personnel information management, employee training, and recruitment. Each module contains specific functionalities, for instance, the personnel information management module includes employee profile management, attendance records, and payroll functions; the employee training module includes training plans and performance evaluations. To meet these needs and optimize system performance, we should store the data of different functional modules in separate databases to achieve data isolation and enhance data security. Therefore, adopting a multi-database design is necessary.

### Concept of Data Warehouse

Within multi-databases, there is another important concept: the data warehouse. A multi-database system means that an enterprise may have multiple databases storing data from different departments or business functions, which may have inconsistent formats and be difficult to integrate.

When applications used by the enterprise need to store data from multiple different databases, the data warehouse plays an important role, similar to a large data center. It is responsible for integrating, cleaning, and transforming data from various databases and then storing it in a unified location for analysis and a comprehensive understanding of the overall situation.

Regardless of which database the data was originally stored in, it can be more conveniently viewed through the data warehouse, allowing the enterprise to gain a more comprehensive understanding of the relationships and trends among the data.

In summary, the role of the data warehouse in a multi-database environment is to help enterprises integrate data, provide a comprehensive data view, and support cross-database data analysis, thereby better managing data and making more informed decisions.

## 2. Use of Data Warehouse in Enterprise Applications

Taking the inventory management system as an example, let’s see how to utilize the data warehouse to optimize business processes and decision-making, to better understand the use of data warehouses in enterprise applications.

Suppose a company uses an inventory management system to manage its supply chain department, inventory management department, and sales department, with each department having its own database for storing relevant business data.

- Supply Chain Department Database: Contains supplier information, purchase orders, delivery records, etc.
- Inventory Management Department Database: Contains inventory records, product information, inbound and outbound records, etc.
- Sales Department Database: Contains customer information, sales orders, delivery records, etc.

By establishing a data warehouse to integrate data from various departments, we can summarize the functions and advantages of the data warehouse in the inventory management system as follows:

1. Data Integration and Unified View:
The data warehouse can integrate data from different databases, such as supply chain, inventory, and sales data. This way, by creating a unified view, company management can view the entire supply chain's operations, including procurement, inventory, and sales, in one place without needing to check each department's database separately.

2. Data Synchronization and Consistency:
The data warehouse serves as the center for data synchronization, ensuring data consistency between different databases. By regularly synchronizing data to the data warehouse, issues of data inconsistency or obsolescence can be avoided. For example, purchase order data from the supply chain department and inbound/outbound record data from the inventory management department can be synchronized in the data warehouse, ensuring the correlation between procurement and inventory.

3. Performance Optimization:
The data warehouse, optimized and indexed, can provide high-performance data querying and analysis, speeding up data access. For instance, company management can quickly analyze key metrics such as inventory turnover rates and supplier performance through the data warehouse, allowing timely adjustments to procurement and sales strategies.

4. Historical Data Storage and Analysis:
The data warehouse stores historical data and supports the analysis and backtracking of historical data, helping to understand business trends and formulate long-term strategic planning. For example, the company can analyze sales and inventory data from the past few quarters to understand sales trends and inventory turnover, and develop future inventory management strategies.

5. Security and Access Control:
The data warehouse can implement fine-grained access control, managing user access permissions to data. For example, employees in the sales department can only access sales data, while employees in the inventory management department can only access inventory data, thereby protecting the company's sensitive information.

By using the data warehouse, the company can better manage its supply chain, inventory, and sales activities, optimize business processes, and make more informed decisions, thus improving efficiency and reducing costs.

### Using Data from the Data Warehouse as Views

In multi-database applications, to avoid potential data inconsistencies or errors caused by direct cross-database queries, it is crucial to choose to use data from the data warehouse as views.

- Synchronization of Business Data Tables Across Multiple Applications:

The data warehouse, as a centralized database for storing data, can be used to synchronize business data tables from multiple applications. Through the process of Extract, Transform, Load (ETL), the business data from each application can be synchronized to the data warehouse, achieving centralized management and a unified source of data.

- Unified Source of User Data Across Multiple Applications:

The data warehouse can also serve as a unified source of user data for multiple applications. By centrally managing user information, including user identities and permissions, a shared user system across multiple applications can be achieved, improving consistency and efficiency in user management.

- Real-Time Data Updates Across Multiple Applications:

The data warehouse can support real-time data updates, ensuring that the data in the views remains current. Through regular data synchronization and update processes, users can always access the latest data.

- Data Security and Privacy Protection Across Multiple Applications:

The views in the data warehouse can implement data security and privacy protection. By setting permissions and access controls, it can ensure that only authorized users can access specific data, thereby protecting sensitive information from unauthorized access.

## 4. Data Warehouse Synchronization Mechanism: Triggers

The data warehouse synchronization mechanism is a key component in ensuring that the data in the data warehouse remains synchronized with the source systems. During the data warehouse synchronization process, we use MySQL triggers to automatically execute specific operations when certain events occur in the database tables, achieving data synchronization.

When using MySQL triggers for data warehouse synchronization, the general steps include:

- Creating Triggers

First, triggers need to be created in the MySQL database. Triggers can be defined for specific tables and events (such as BEFORE INSERT, AFTER UPDATE, AFTER DELETE).

- Writing Trigger Logic

When creating triggers, the logic code for the trigger needs to be written, defining the operations that should be executed when the triggering event occurs. These operations typically include extracting, transforming, and loading data from the source system tables into the corresponding tables in the data warehouse.

- Activating Triggers

Once the trigger is created and bound to the corresponding tables and events, the trigger will be activated when these events occur, executing the defined logic code.

- Data Synchronization

The logic code of the trigger will execute data synchronization operations, ensuring that the data in the data warehouse remains synchronized with the source systems. This helps maintain data consistency and real-time accuracy.

- Performance Optimization

When using MySQL triggers for data synchronization, performance optimization must be considered to ensure that the trigger logic is concise and efficient, avoiding unnecessary impacts on database performance.

In summary, the principle of triggers synchronizing data in the data warehouse is based on monitoring data change events and executing corresponding synchronization operations when events occur, ensuring that the data in the data warehouse remains consistent with the source systems. By designing and using triggers appropriately, real-time data synchronization can be achieved, improving the data quality and timeliness of the data warehouse.